Esther Seyffarth


2022

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The Maaloula Aramaic Speech Corpus (MASC): From Printed Material to a Lemmatized and Time-Aligned Corpus
Ghattas Eid | Esther Seyffarth | Ingo Plag
Proceedings of the Thirteenth Language Resources and Evaluation Conference

This paper presents the first electronic speech corpus of Maaloula Aramaic, an endangered Western Neo-Aramaic variety spoken in Syria. This 64,845-word corpus is available in four formats: (1) transcriptions, (2) lemmatized transcriptions, (3) audio files and time-aligned phonetic transcriptions, and (4) an SQLite database. The transcription files are a digitized and corrected version of authentic transcriptions of tape-recorded narratives coming from a fieldwork trip conducted in the 1980s and published in the early 1990s (Arnold, 1991a, 1991b). They contain no annotation, except for some informative tagging (e.g. to mark loanwords and misspoken words). In the lemmatized version of the files, each word form is followed by its lemma in angled brackets. The time-aligned TextGrid annotations consist of four tiers: the sentence level (Tier 1), the word level (Tiers 2 and 3), and the segment level (Tier 4). These TextGrid files are downloadable together with their audio files (for the original source of the audio data see Arnold, 2003). The SQLite database enables users to access the data on the level of tokens, types, lemmas, sentences, narratives, or speakers. The corpus is now available to the scientific community at https://doi.org/10.5281/zenodo.6496714.

2021

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Implicit representations of event properties within contextual language models: Searching for “causativity neurons”
Esther Seyffarth | Younes Samih | Laura Kallmeyer | Hassan Sajjad
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

This paper addresses the question to which extent neural contextual language models such as BERT implicitly represent complex semantic properties. More concretely, the paper shows that the neuron activations obtained from processing an English sentence provide discriminative features for predicting the (non-)causativity of the event denoted by the verb in a simple linear classifier. A layer-wise analysis reveals that the relevant properties are mostly learned in the higher layers. Moreover, further experiments show that appr. 10% of the neuron activations are enough to already predict causativity with a relatively high accuracy.

2020

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Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation
Esther Seyffarth | Laura Kallmeyer
Proceedings of the 28th International Conference on Computational Linguistics

English verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation’s felicity by simulating it. Beyond the prediction task, we use the classifier results as a source for a manual annotation step in order to identify new, unseen instances of each alternation. This is possible because existing alternation datasets contain positive, but no negative instances and are not comprehensive. Over several sequences of classification-annotation steps, we iteratively extend our sets of alternating verbs. Our hybrid approach to the identification of new alternating verbs reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification. Due to the success of semi-supervised and unsupervised features, our approach can easily be transferred to further alternations.

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Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories
Kilian Evang | Laura Kallmeyer | Rafael Ehren | Simon Petitjean | Esther Seyffarth | Djamé Seddah
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

2019

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Identifying Participation of Individual Verbs or VerbNet Classes in the Causative Alternation
Esther Seyffarth
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

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Modeling the Induced Action Alternation and the Caused-Motion Construction with Tree Adjoining Grammar (TAG) and Semantic Frames
Esther Seyffarth
Proceedings of the IWCS 2019 Workshop on Computing Semantics with Types, Frames and Related Structures

2018

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Verb Alternations and Their Impact on Frame Induction
Esther Seyffarth
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop

Frame induction is the automatic creation of frame-semantic resources similar to FrameNet or PropBank, which map lexical units of a language to frame representations of each lexical unit’s semantics. For verbs, these representations usually include a specification of their argument slots and of the selectional restrictions that apply to each slot. Verbs that participate in diathesis alternations have different syntactic realizations whose semantics are closely related, but not identical. We discuss the influence that such alternations have on frame induction, compare several possible frame structures for verbs in the causative alternation, and propose a systematic analysis of alternating verbs that encodes their similarities as well as their differences.

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AET: Web-based Adjective Exploration Tool for German
Tatiana Bladier | Esther Seyffarth | Oliver Hellwig | Wiebke Petersen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)